In this interview from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event, Glean co-founder and CEO Arvind Jain joins theCUBE’s John Furrier to unpack what’s really working in enterprise AI today and what comes next. Jain explains why knowledge access remains the first successful AI use case at scale and how Glean’s enterprise search brings AI into everyday work. He details the past year’s lessons with AI agents – from the need for guardrails, security, evaluation and monitoring to democratizing agent building so business owners (not just data scientists) can create production-grade agents.
The conversation dives into Glean’s vision of the enterprise brain powered by an enterprise graph, highlighting the importance of deep context, human workflows and behavior to reduce “noise” and drive outcomes. Jain outlines core building blocks – hundreds of enterprise integrations and a growing actions library – that let agents securely read company knowledge and take actions across systems (e.g., CRM updates, HR tasks, calendar checks). He discusses how organizations are standing up AI Centers of Excellence, prioritizing “top 10–20” agents across functions like engineering, support and sales, and why a horizontal AI data platform that unifies structured and unstructured data – accessed conversationally and stitched together via standards like MCP – sets the foundation for AI factory-scale operations. Looking ahead, Jain says Glean’s upgraded assistant is evolving from reactive tool to proactive companion that anticipates tasks and accelerates productivity.
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Dan Wright, Armada
In this interview from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event, Glean co-founder and CEO Arvind Jain joins theCUBE’s John Furrier to unpack what’s really working in enterprise AI today and what comes next. Jain explains why knowledge access remains the first successful AI use case at scale and how Glean’s enterprise search brings AI into everyday work. He details the past year’s lessons with AI agents – from the need for guardrails, security, evaluation and monitoring to democratizing agent building so business owners (not just data scientists) can create production-grade agents.
The conversation dives into Glean’s vision of the enterprise brain powered by an enterprise graph, highlighting the importance of deep context, human workflows and behavior to reduce “noise�� and drive outcomes. Jain outlines core building blocks – hundreds of enterprise integrations and a growing actions library – that let agents securely read company knowledge and take actions across systems (e.g., CRM updates, HR tasks, calendar checks). He discusses how organizations are standing up AI Centers of Excellence, prioritizing “top 10–20” agents across functions like engineering, support and sales, and why a horizontal AI data platform that unifies structured and unstructured data – accessed conversationally and stitched together via standards like MCP – sets the foundation for AI factory-scale operations. Looking ahead, Jain says Glean’s upgraded assistant is evolving from reactive tool to proactive companion that anticipates tasks and accelerates productivity.
play_circle_outlineArmada Secures $250 Million Investment from Microsoft and Founders Fund to Partner with SpaceX's Starlink for Global AI Connectivity
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play_circle_outlineDan Wright explains distributed computing and its importance for Armada's strategy.
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play_circle_outlineTransforming Critical Industries: The Role of Edge Data Processing and AI Models in Modern AI Factory Infrastructure
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play_circle_outlineImportance of distributed AI factories for national manufacturing and supply chain security.
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play_circle_outlineStrategy to build a comprehensive partner ecosystem across various technology layers.
In this interview from theCUBE + NYSE Wired: AI Factories event, Dan Wright, co-founder and chief executive officer of Armada, joins theCUBE’s John Furrier to discuss the paradigm shift toward distributed computing and the rise of the "hyper-converged edge." Wright explains how Armada is redefining infrastructure by deploying modular AI factories to remote environments, from oil rigs to the middle of the ocean, effectively bringing cloud-like experiences to the physical world. The segment highlights Armada’s significant backing and strategic collaborations wi...Read more
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What recent achievements and partnerships has the company accomplished?add
What is the current strategy for deploying distributed computing infrastructure and what factors are influencing its evolution?add
What are the key challenges and advancements in processing data generated at the edge, and how is Armada addressing them?add
What capabilities does advanced AI, automation, and robotics provide for the manufacturing and supply chain industries?add
What are the company's ambitions and vision for becoming a leader in the edge computing market?add
>> Welcome back around to theCUBE here at our NYSE studios. I'm John Furrier, host of theCUBE. This is our AI factory series where we're talking to the leaders who are building out the next generation large scale systems, networks, and ultimately the infrastructure on the AI side that's going to create a lot of value and also create value extraction for the new kinds of AI-native applications. Dan Wright is here, co-founder and CEO of Armada here on theCUBE coming in remote from the Bay Area. Great to see you. Thanks for coming in.
Dan Wright
>> Thanks for having me. Great to see you, John.
John Furrier
>> You guys are doing some really cool stuff over there. You're kind of setting the table. I can kind of see the dots connecting. We'll get into it, but explain to what you guys are doing. You're really creating the system for what I would say AI factories infrastructure like trend to essentially connectivity all over the world and obviously connecting even from space. You got to deal with SpaceX and Starlink. And so the world's networked, network is the operating system for these AI factories, but that's just within the data center. When you go outside the data center, you guys are doing some compelling work. Explain what you guys are doing and some of the things you got momentum around.
Dan Wright
>> Yeah. The whole idea with Armada and what we're doing is distributed compute. So taking AI factories to the edge, utilizing whatever power is available and co-locating them with the data as well as autonomous technologies and robotics and drones and other capabilities that people want to deploy now at scale in critical industries like oil and gas, mining, telco, but then also in the public sector, working with the Navy, for example, this is public, where we're out there working with them, doing cutting edge AI, cutting edge autonomy in the middle of the ocean or any other edge location.
John Furrier
>> How big is the company? Give some stats on just some of the data, how big you guys are, funding and progress on the product.
Dan Wright
>> Yeah. So we've raised about $250 million from some of the top investors in the world, Founders Fund, Luxe Capital, Valor, many others, as well as Microsoft made a strategic investment in the company. And we are partners with them. And we also work very closely with SpaceX. And what we have done is every time SpaceX rolls out in a new geo with Starlink, we're the first mover in terms of the infrastructure because we can deploy our modular AI factories faster, cheaper, more flexibly than anybody else. And in a few years since we founded the company, we're now well over 300 people and we're growing very fast. A lot of our people are up in Bellevue near Seattle and we were actually just named one of Seattle's top startups. But right now, I'm here in the Bay Area and we have an office here in the Presidio and we're all over the world. We even have a presence in UAE.
John Furrier
>> Awesome. Well, thanks for doing that. One of the things I want to get into is this, if you look at the roadmap, and Jensen Wong is really good at this at all as events. He lays very transparent about the roadmap, the supply chain, but also for the ecosystem. Obviously AI agents, physical AI is kind of like the three levels. Physical AI, they talk a lot about with robotics. We see robo taxis. You mentioned SpaceX and Starlink, a lot of these remote large scale computing environments, highly accelerated. We are in an era of AI factories, which are essentially new kinds of data centers, purpose-built for the kinds of scale requirements for the tokens, for the intelligence, for the agents, and ultimately for the physical digital kind of convergence of devices. Okay, great. Now to make all that work, you got to network them. You got to have compute. That's obvious. Compute's been, checked the box there. We're getting more and more of that, but there's a lot more going on. Explain your vision on this because I love how you have this distributed computing philosophy applied to the new architecture of factories, because the game is still the same in distributed computing, but the factories are a little bit different. Explain what's going on and what your strategy is.
Dan Wright
>> Yeah. Our whole strategy is to be the fastest full stack infrastructure in the world to deploy anywhere in the world, enabling, just if you're talking about distributed compute. And the reason is there's a few fundamental things that are happening in the world that are going to continue to accelerate as we go through the rest of this decade and beyond. One is you have connectivity becoming ubiquitous. Starlink didn't exist five years ago. Actually, it launched in public beta in November of 2020. Now it's in well over 150 countries. It's expanding every week, and the service is continuing to get better and better. But you know, we also have private 5G with technologies like Nokia and Ericsson. We have SD-WAN with Cradlepoint and Peplink and others. And so finding the right way to enable perfect connectivity in these different locations that's optimized both for performance and cost and security has never been easier and it's only going to continue to get easier. So ubiquitous connectivity, that's one. The second thing is you have data exploding at the edge right now. 80% of data is created outside of and stored outside of traditional hyperscale data centers. And the question is, how do you process that data efficiently to enable technologies like robotics, like real-time response using drone technology, like real world AI at the edge? And then the third thing that's happening is that the AI models are becoming more and more powerful and people are looking at agents. How do I apply agents not just to the back office or to maybe coding some software, but to actually solve important problems on the ground in operations for critical industries and then for things like defense. And we're enabling those things. And what's unique about Armada is that we can do it, again, faster, better, cheaper, more flexible than anybody else. And we do it full stack so that it's like a cloud-like experience at the edge. Anybody can use our technology, and it's as simple as using the cloud.
John Furrier
>> Yeah. I think you guys are on something really big here and we're going to be putting out a report. I'm going to be put a report in February around a thesis that we have. And I think I want to get your reaction, because I think you're right on the trend line there, is that the edge will be hyper converged. And what I mean is that all the wireless protocols, whether it's licensed spectrum or unlicensed spectrum, will collapse in with AI factories as a compute slash intelligent device to connect wireless so they can mesh up and also provide seamless access to anyone coming in, which is pretty obvious, and that's not too hard to understand. But what that enables with a data centric model-specific or model-agnostic approach is that once you connect those factories, a lot of new things can happen. So models could move to the edge, new kinds of provisioning of services could be enabled. A whole new set of use cases emerges versus the old classic, move data to the edge node, processes that highly available or high availability. I mean, old data models change. So now you have a new data-centric edge versus say a voice splitted with data, an old telecom approach. And then also think about like, how do you bring AI to telecom, to networking, like real AI? So I think the model side of this really changes the game to the networking equivalent. What's your reaction to that, and how would you explain that to someone as the kind of the new edge, AI on the edge, if you will?
Dan Wright
>> Yeah. I think you're on the money there, and I'd explain it to somebody in a couple of different ways, which when you take a step back and think about it, it becomes obvious that this edge, this hyper-converged edge is inevitable. One is you look at where the data is, follow the data. And there's more and more sensors everywhere, whether you're talking about critical infrastructure, dams and bridges or even cell towers, pipelines, mining conveyors, you go on and on. All of these things have sensors all over them that are generating massive amounts of data. In the public sector, you have drones being used as a first responder. For example, we work with a lot of states that are using it to respond to natural disasters like the fires that we saw in Southern California last year or the recent flooding in Alaska. People want to be able to use all of that data that's being generated in real time. And they also want to be able to take the most powerful AI models and run those in real time at the edge, and so that requires distributed infrastructure to enable that. And then the other thing is you got to think about, well, what is the biggest blocker to AI today? The biggest blocker to AI today is the power, and power, energy is also distributed. And so why wouldn't we utilize all the energy that's available, whether it is stranded natural gas or it is excess energy that might be available at cell towers or different utilities. And so our whole thesis is just as you said, that the edge is going to become something that is not an edge thing. It's going to become the whole thing. And that's because ultimately that's where all the data and the power is, and it's going to be the most efficient architecture as we move forward.
John Furrier
>> Yeah. We got our new studio here in New York Stock Exchange, and I'll tell you that I had some conversation here in New York about all the wasted energy in the buildings. So I'd envision a metro factory. And again, this is distributed computing. You could have many nodes in the network as long as it's kind of traversing properly and connected properly. This is why I want to ask you about the networking piece, because if you look at the models, right, I was interviewing what's come out of the robotics series we're doing is these entrepreneurs were building these robotics for say things like life sciences, very use case, a wet lab, I got a robot that's had some precision, I'm swapping in and out some things in that test lab. Turns out that this one entrepreneur built a great robot and turns out he's selling it into other verticals because he used open source software. And what that basically means is like in that scene in the matrix where he plugs in and says, "Upload how to fly a helicopter," and instantly the domain-specific skills speak to that. So you're seeing the software hardware relationship now coming to say the edge where if there's a drone or say first responder or say some user retail example, why wouldn't you want to just insert domain expertise in any device, whether it's a robot or an edge server? This is kind of where the software's going, Dan.
Dan Wright
>> A hundred percent, right? And you think about what is exciting about that. I mean, you mentioned healthcare, life sciences, those are some of the most exciting use cases, but then you think about other things that are going on in the world. An example I like to give people is manufacturing, right? We can't hire enough people and train them to do all of the manufacturing that we need to do in America in order to de-risk our supply chain versus foreign adversaries. And so the only way to actually do that is to do it through robotics. And that is what this technology enables. Think about an army of highly skilled manufacturing talent that is suddenly at your disposal and works 24/7. That's an exciting thing for, I think both these companies that are deploying this technology, but also for the country, and then other areas. We work with a lot of top mining companies that are focused on de-risking the supply chain for critical minerals, but similar dynamic there. It's like, how do you hire enough skilled people to be able to produce what we need to produce given that today, China has 90% market share when it comes to the supply chain for, for example, rare earth magnets that are used to create smartphones and drones and electric vehicles. The only way we're going to be able to do that is through advanced AI, automation and robotics at the edge in order to scale our capacity. And that is what full stack, hyper-converged edge, just as you said, enables.
John Furrier
>> It's interesting, that manufacturing example. One thing that's also come up just to riff on that with you a little bit is that a lot of the manufacturing lines don't have to be purpose-built for a specific thing. You can have multifunction capabilities in manufacturing. You can actually run a line, change the SKU, change the product within reasons versus the old days of build the factory, line up the robotics, static, supply chain, build. So much more versatility. I mean, basically it points out that your TAM is massive. So what's your strategy? Obviously, I mean, every market is going to have some physical digital automation. There'll be hardware, software, robotics, there'll be a version of that, whether that's a retail experience for someone walking into a store, computer vision, download my models, connect to my home PC, what's in my shopping cart, oh, it's an IL5, to autonomous driving. These are real, this is a massive market. What's your strategy? Are you guys going for the whole enchilada? Are you guys going to come in and sequence to a position and then kind of take territory? What's the vision?
Dan Wright
>> Yeah. Yeah, so we have big ambitions and the idea is to be the hyperscaler for the edge. That's the vision for the company. And the way that we're going to do that is to follow what actually worked for the hyperscalers decades ago, which is you create an ecosystem around a platform that enables lots of different workflows, different use cases. And the way that we do that is that we have a bunch of partners, whether it's at the connectivity layer, whether it's at the application layer through our marketplace that can plug in to our platform, and then they also make money in this whole equation by adding value for these end customers. And so whether that is a partner, like we just announced a partnership with OpenAI, bringing those models to the edge, or any other model company, or it is a traditional kind of industry-specific provider like Halliburton, which we also have a partnership with, this is public. We want to be the edge partner that brings all of those capabilities to the edge. And that's also true for even the traditional hyperscalers. So for example, we have a close partnership with Microsoft. We embrace technologies like Azure Stack and their latest models and give them a home and an infrastructure where those can run all at the edge.
John Furrier
>> Explain the full stack approach. I'm looking at your partner strategy here. You got channel partners, which is essentially people in the domain. You got hardware, infrastructure, technology, and then industry. Industry, is that the ecosystem? And what's the difference between a technology partner and say an infrastructure and a hardware partner?
Dan Wright
>> Yeah, good question. I mean, with the infrastructure and the hardware partner, it's a pretty simple proposition, which is number one, we bundle their technology with our own and then distribute it as a end product, end solution, I should say, to the customer. Because ultimately, the people that are working in the factories, the people that are working in the hospitals, the people that are working on the oil rigs, just as an example, they are not IT people typically. They are people that are just trying to solve a job, do a job better. And what we do is we take this full stack solution and we give them superpowers. And so that's how we want it to work. The other nice thing with these hardware providers is that we then put that hardware in our marketplace and it's like one-stop shopping for the edge for our customers. For example, I mentioned that we have a partnership with Starlink. Customer wants any Starlink terminal, any plan, they can go into our marketplace, boom, I want it over here, I wanted these five locations, and we just make it happen. So that is one. The second thing is with the software companies, like for example, we have a partnership with Halliburton for their landmark applications. They have all of the deep subject matter expertise from decades working in oil and gas. We don't have to reinvent the wheel. Instead, what we do is we make those capabilities available in our marketplace. And in the same way, it's boom, as a customer make one click, I can run that at the edge natively, and then I get the value of that application, which I already understand because I use it at my more connected sites in these more remote edge sites. And then the same thing is true with these cutting edge AI model providers, not just the big ones, but also emerging ones that want to create models that solve really high value use cases at the edge. We create an ecosystem around there and then we give them low touch distribution where the second they publish that model in our marketplace, it's available to all our customers all over the world.
John Furrier
>> So since your physical AI kind of game here, whether it's robotics or whether it's whatever industry, oil and gas and others, you're relying on partners in the ecosystem to build those out on your behalf, similar to what AWS did with platform as a service and infrastructure, let the SaaS market build the solution. Is that the right way to think about it?
Dan Wright
>> Yeah, that's the default is that if there is a existing model or application that solves a given problem, we just integrate and we partner, because again, we don't want to reinvent the wheel. As you said, there's infinite use cases here that are high value and that are time-sensitive. And so we want to tackle them as quickly as possible. In certain cases, there's nobody else out there that has a subject matter expertise or that has built the application or the model. And in that case, we will build it ourselves, but we try as much as possible to partner because there are so many of these use cases. And what we want to be is the brain, whether you're talking about an application or a model or a robot or a drone, we're the brain that makes it all work and coordinates all of this together all over the world.
John Furrier
>> So obviously infrastructure, NVIDIA, big partner, I mean, they're just awesome. Everything you're talking about, it's on their roadmap, fiscal AI. On the hardware, I noticed you got sensors, you mentioned sensors earlier. That's highly involved. I mean, autonomous, anything has sensors. What are some of the hardware areas you're looking at that you're looking to recruit partners on or see traction immediately? And what else is beyond Nvidia? So they have a ton of edge going on with the Nokia deal. That was announced in DC. That makes a lot of sense to me. There's a huge national footprint focus in the US. Again, that ties nicely. Alcatel, Lucent, Bell Labs, that checks the box for me. It feels great. NVIDIA loves it. Where are your areas of focus for some of those key hardware partners? What's the new area you need to lock in?
Dan Wright
>> Yeah, great question. So we're already partnering with Nvidia and Nokia just to name a couple, but we want to partner with any hardware customer that our companies that we work with, as well as with the US government and its allies, that they're adopting. And then increasingly, we're actually being asked, for example, what's the best connected camera by our partners, by our customers. And so we can also help them when they're looking for something. But in many cases, you take a large oil and gas company or you take a large manufacturing company, they already have sensors all over the oil rigs, all over the refineries, all over the factories. In the case of the factories, they have cameras that they're using today. What we do is we just plug into those and we integrate with those because that is the lowest friction way to create value for the customer. And what we're about at Armada is really fast time to value and creating so much value that then we want to go onto the next workload and the next workload and the next workload. And then what you get in a year or maybe 18 months is you have a connected rig, you have a connected factory, because that is ultimately where this becomes game changing for all of these types of companies where you have everything connected. And actually, if you open up our platform, the front page to the platform is what we call our fleet map. And in that, we show you all of your connected assets all over the world and then you can manage them separately, I should say, together in your network operation center, your NOC. And what we see is in the future, just like Jensen says, every company is going to have its own AI factory and every country's going to have its own AI factory. But even down to the site level, you'll be able to see all of your connected sites, you'll be able to manage them centrally from a single pane of glass, and Armada is going to be that pane of glass that enables that.
John Furrier
>> Well, Dan, you and your team have great vision. I totally agree with this whole network of factories. Distributed computing is a paradigm that you just swap out computer server with a bunch of other servers, large scale stuff, and connect them to something else that's a factory and you have a network factory model. And again, the intelligence and the AI is the unique thing that brings it to the network that we've never seen before.
Dan Wright
>> Right. Yeah. Well, thank you. Yeah. We totally see things the same way. I think that it's going to be amazing and it's going to blow people's minds where the world is headed over the next few years, and we're really excited to see it happen.
John Furrier
>> Final question for you. Obviously in the cloud game, again, this is my opinion, but I'll just say it. The telco guys really dropped the ball on the cloud. They could have extracted a lot of value there. Instead, Apple and Google Marketplaces have sucked all that value out. I think with the factories, if you own the networks and have the data like a telco or a carrier, you can really make the edge work. And Starlink, Prometheus at AWS, these are things coming, terrestrial and landline, ethernet with wireless spectrum and licensed spectrum. There's a huge opportunity there for value creation, extraction for networking operators. What's your reaction to that?
Dan Wright
>> I totally agree. And I'll take it one step further, which is I think it's almost a mandate for telcos at this point because you probably saw recently, Starlink just purchased $18 billion of Spectrum from EchoStar. Five years from now, they're going to have connectivity everywhere that's as good as fiber in the ground. Well, what do you do if you're a telco? You utilize the assets that you have. You already have a very established presence in these markets around the world. You already have distributed power. What is the best thing to do? Enable distributed compute, enable distributed AI factories and become a provider of intelligence to all of these different parts of the world and critical industries. And so I think we're seeing that. I think as we go through the next few months leading up to Mobile World Congress and then throughout 2026, you're going to see that continue to accelerate, because whoever makes that transition the fastest has a huge, huge opportunity, and whoever doesn't is at risk.
John Furrier
>> I mean, the hyperconverged network brings up new services. Think about the corporate services they could provide through licensed spectrum. I'm on a VPN. I want to have an app. I'm in public, public, private, hype, multi-domain, multi-tenant, large scale clouds coming. Sovereignty.
Dan Wright
>> Sovereignty. I mean, and it's happening all over the world. People don't want just AI factories. They want distributed AI factories. And even more than that, they want sovereign distributed AI factories. So that is a huge opportunity and one that I think telco is really well situated.
John Furrier
>> Well, you got a lot of great investors, Dan, and this won't be our last conversation. I think what you got is at least a good five-year headstart, in my opinion, from everybody else. So congratulations. And thanks for coming on our AI factory series.
Dan Wright
>> Thanks, John. Really enjoyed it. And yeah, looking forward to many more conversations.
John Furrier
>> I'm John Furrier with theCUBE. This is our AI factory series. Again, it's just the beginning, scratching the surface. Jensen said it's going to be a complete reset of the semiconductor industries. The growth will be 10X. It's going to grow significantly. As the AI infrastructures continues to accelerate, the next waves of scale kicks in. And again, edge, core, cloud, space, networking is going to be a big part of it. So we'll keep you covered here on theCUBE. Thanks for watching.
John Furrier